Quantum annealing technology is of interest to government and commercial entities, as it could provide enormous performance improvements in solving hard classical optimization problems such as resource allocation and scheduling, planning, navigation, circuit and network design and validation, and pattern recognition, etc.
An important limitation of existing quantum annealing technology for classical optimization is the limited connectivity of the physical spin qubit hardware. Since each qubit nominally represents a binary variable in the optimization problem to be solved, and the connections made between them encode that problem (in the form of constraints), limited physical hardware connectivity translates to a limited complexity of the optimization problems that can be posed on that hardware. Some existing systems attempt to solve this problem using a technique known as “embedding,” in which physical spin qubits with small connectivity are grouped together to form “logical spin qubits” with higher connectivity.
In some existing systems, hardware coupling between spin qubits is engineered using additional “coupler” qubits, with each pairwise 2-spin coupling implemented by a single coupler qubit, inductively coupled to two spins. Making a large number of such direct inductive connections to a single quantum spin is simply not possible due to the small size of the qubits, the corresponding geometrical constraints, and the large inductive loading of the spin that would result. Thus, existing systems can support at most six pairwise connections to each spin.